Associative Learning

Sentience criterion; capacity occurs even in gene regulatory networks and non-neural morphogenetic agents.

Neighborhood — ranked by edge-count

framework

Eight Sentience Criteria (Crump et al.)
cites
Quantitative, flexible framework for assessing sentience: nociception, sensory integration, analgesia, trade-offs, self-protection, learning, preference.

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Learningconcept0.834
Inference of parameters encoding contingencies of the world (e.g., likelihood matrix A) at slower timescale than perception.
Associative Memoryconcept0.828
Memory system that stores patterns in connection weights and recalls them from partial or noisy cues; property of Hopfield networks and evolved networks
associative addressingconcept0.809
Tuples are referenced by matching typed fields, akin to a relational database.
Perceptual Learningconcept0.805
Process of inferring causes of sensory information; unified with value learning as integral aspects of free energy minimization.
Associative memory modelframework0.803
Epistemic Learningconcept0.799
Learning model parameters through curious, uncertainty-reducing behavior; reducing ignorance about contingencies
Supervised Learningframework0.796
Learning through physical changes in mechanical networks, as an example of learning outside neural systems.
Meta-learningconcept0.794
The capability of GPT-3 to learn tasks from few-shot prompts during runtime.